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A Review of Scene Representations for Robot Manipulators

arXiv 2301.11275 ↗First posted 22 Dec 2022cs.RO cs.CVcited by 0

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Abstract (arXiv)

For a robot to act intelligently, it needs to sense the world around it. Increasingly, robots build an internal representation of the world from sensor readings. This representation can then be used to inform downstream tasks, such as manipulation, collision avoidance, or human interaction. In practice, scene representations vary widely depending on the type of robot, the sensing modality, and the task that the robot is designed to do. This review provides an overview of the scene representations used for robot manipulators (robot arms). We focus primarily on representations which are built from real world sensing and are used to inform some downstream robotics task.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 100 references that are in the map. First posted on arXiv 22 Dec 2022.

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29 of 100 references.

References · 29

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17 more
  1. 2021Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representationscited by 80
  2. 2016Shape Completion Enabled Robotic Graspingcited by 79
  3. 2019DensePhysNet: Learning Dense Physical Object Representations via Multi-step Dynamic Interactionscited by 61
  4. 2022Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fieldscited by 58
  5. 2020PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Cloudscited by 39
  6. 2020Learning 3D Dynamic Scene Representations for Robot Manipulationcited by 39
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  12. 2017Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learningcited by 15
  13. 2018Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packingcited by 12
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  17. 2022Differentiable Collision Avoidance Using Collision Primitivescited by 4

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